Our method was implemented with \(V=50\) , cluster proportion \(M/N=0.1\) , and \(\phi (N)=2\)  (AIC). In the iterative weighting, we further set iteration number \(R=150\)  and stop criteria when the weights do not change for 10 steps. E-Divisive was implemented via \(ecp\)  package with the tuning parameter \(\alpha =1\) , \(R=499\)  which was advocated in the paper. KernelMCP was implemented by Python package named \(Chapydette\)  using the default setting (Gaussian kernel with the Euclidean distance, bandwidth = 0.1, and \(\alpha =2\) ). For MultiRank, we implement R codes provided in the supplementary file of ([1]}).
